Video Recommendation System Using Search and Download Data
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Solution Overview
Problem
Current video program recommendation systems are limited by relying solely on data from digital video recorders and subscription services, failing to account for video content watched outside these platforms, such as DVDs or online downloads, and require user training and time to provide relevant recommendations.
Innovation Solution
A recommendation system that utilizes data from web services and online video content providers to identify associations between video programs based on user interactions, including search queries and downloads, to provide personalized recommendations, incorporating data from various sources and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the subscription service uses only data from digital video recorders and television broadcast systems to make recommendations, then the system complexity remains manageable, but the scope of information available for recommendations is limited
Solution Approach 1:
The patent combines multiple data sources including digital video recorder data, subscription service data, web service interaction data, and online video content provider data into a unified recommendation system. This merging of previously separate systems allows the service to access a much broader scope of information about user viewing habits across all platforms, not just traditional TV broadcasting.
Solution Approach 2:
The recommendation system is designed to handle multiple types of data sources and interaction types universally. It processes data from digital video recorders, web services, and online content providers through a single unified architecture that can accommodate various input formats and interaction types, making the system multi-functional and adaptable to different data sources.
2Measurement precision
If the subscription service requires user training through ratings to provide relevant recommendations, then the accuracy of recommendations improves, but the time required before valuable recommendations are provided increases
Solution Approach 1:
The system performs preliminary analysis of user interaction data from web services and online content providers to pre-establish recommendation patterns before formal user training begins. By analyzing search queries, downloaded content, and viewing patterns from multiple sources in advance, the system can provide initial relevant recommendations sooner rather than waiting for extensive user rating data to accumulate.
Solution Approach 2:
The system implements continuous feedback loops that analyze user interactions across all data sources in real-time. Rather than waiting for explicit user ratings, the system continuously monitors viewing patterns, search behavior, and content consumption from digital video recorders, web services, and online content providers, using this feedback to dynamically adjust and improve recommendation accuracy over time.
3Adaptability or versatility
If the subscription service only monitors television programming watched through the broadcast system, then the data collection process remains simple, but the ability to understand comprehensive user viewing habits is reduced
Solution Approach 1:
The patent merges data collection from multiple previously separate systems including digital video recorders, web service interactions, and online video content provider platforms into a unified data collection architecture. This integration allows the service to comprehensively track user viewing habits across all platforms - traditional TV, online streaming, downloaded content, and search behavior - providing a complete picture of user preferences rather than fragmentary data from a single source.
Data Source
AI summary
A system and method for recommending video programs to a user comprising determining a first video program that is of interest to a user and then determining a second video program to recommend to the user, the second video program being determined from a recommendation database assembled by analyzing access logs from one or more search engines or online video content providers.